




import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 							UpperCamelCase_       (					UpperCAmelCase__						,   unittest.TestCase	):

    '''simple docstring'''






    UpperCAmelCase__				    = UnCLIPImageVariationPipeline
    UpperCAmelCase__				    = IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
    UpperCAmelCase__				    = IMAGE_VARIATION_BATCH_PARAMS

    UpperCAmelCase__				    = [
        '''generator''',
        '''return_dict''',
        '''decoder_num_inference_steps''',
        '''super_res_num_inference_steps''',
    ]
    UpperCAmelCase__				    = False


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->Tuple:





           '''simple docstring'''

           return 32


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->str:





           '''simple docstring'''

           return 32


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      str)		->Union[str, Any]:





           '''simple docstring'''

           return self.time_input_dim


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->Any:





           '''simple docstring'''

           return self.time_input_dim * 4


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->Union[str, Any]:





           '''simple docstring'''

           return 100


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->Dict:





           '''simple docstring'''

           A__ 					=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')
           return tokenizer


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->List[str]:





           '''simple docstring'''

           torch.manual_seed(0)
           A__ 					=       CLIPTextConfig(
               bos_token_id=0   ,					eos_token_id=2   ,					hidden_size=self.text_embedder_hidden_size   ,					projection_dim=self.text_embedder_hidden_size   ,					intermediate_size=37   ,					layer_norm_eps=1e-05   ,					num_attention_heads=4   ,					num_hidden_layers=5   ,					pad_token_id=1   ,					vocab_size=1_000   ,					)
           return CLIPTextModelWithProjection(UpperCAmelCase__)


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->List[Any]:





           '''simple docstring'''

           torch.manual_seed(0)
           A__ 					=       CLIPVisionConfig(
               hidden_size=self.text_embedder_hidden_size   ,					projection_dim=self.text_embedder_hidden_size   ,					num_hidden_layers=5   ,					num_attention_heads=4   ,					image_size=32   ,					intermediate_size=37   ,					patch_size=1   ,					)
           return CLIPVisionModelWithProjection(UpperCAmelCase__)


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->str:





           '''simple docstring'''

           torch.manual_seed(0)

           A__ 					=       {
               '''clip_embeddings_dim''': self.text_embedder_hidden_size,
               '''time_embed_dim''': self.time_embed_dim,
               '''cross_attention_dim''': self.cross_attention_dim,
           }

           A__ 					=       UnCLIPTextProjModel(**UpperCAmelCase__)
           return model


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->List[str]:





           '''simple docstring'''

           torch.manual_seed(0)

           A__ 					=       {
               '''sample_size''': 32,
               # RGB in channels
               '''in_channels''': 3,
               # Out channels is double in channels because predicts mean and variance
               '''out_channels''': 6,
               '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
               '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
               '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
               '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
               '''layers_per_block''': 1,
               '''cross_attention_dim''': self.cross_attention_dim,
               '''attention_head_dim''': 4,
               '''resnet_time_scale_shift''': '''scale_shift''',
               '''class_embed_type''': '''identity''',
           }

           A__ 					=       UNetaDConditionModel(**UpperCAmelCase__)
           return model


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->List[Any]:





           '''simple docstring'''

           return {
               "sample_size": 64,
               "layers_per_block": 1,
               "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
               "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
               "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
               "in_channels": 6,
               "out_channels": 3,
           }


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->List[str]:





           '''simple docstring'''

           torch.manual_seed(0)

           A__ 					=       UNetaDModel(**self.dummy_super_res_kwargs)
           return model


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->Optional[Any]:





           '''simple docstring'''

           torch.manual_seed(1)

           A__ 					=       UNetaDModel(**self.dummy_super_res_kwargs)
           return model


    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->int:





           '''simple docstring'''

           A__ 					=       self.dummy_decoder
           A__ 					=       self.dummy_text_proj
           A__ 					=       self.dummy_text_encoder
           A__ 					=       self.dummy_tokenizer
           A__ 					=       self.dummy_super_res_first
           A__ 					=       self.dummy_super_res_last

           A__ 					=       UnCLIPScheduler(
               variance_type='''learned_range'''   ,					prediction_type='''epsilon'''   ,					num_train_timesteps=1_000   ,					)

           A__ 					=       UnCLIPScheduler(
               variance_type='''fixed_small_log'''   ,					prediction_type='''epsilon'''   ,					num_train_timesteps=1_000   ,					)

           A__ 					=       CLIPImageProcessor(crop_size=32   ,					size=32)

           A__ 					=       self.dummy_image_encoder

           return {
               "decoder": decoder,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
               "text_proj": text_proj,
               "feature_extractor": feature_extractor,
               "image_encoder": image_encoder,
               "super_res_first": super_res_first,
               "super_res_last": super_res_last,
               "decoder_scheduler": decoder_scheduler,
               "super_res_scheduler": super_res_scheduler,
           }


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any]   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      Union[str, Any]=0   ,					UpperCAmelCase__ :      Dict=True)		->Tuple:





           '''simple docstring'''

           A__ 					=       floats_tensor((1, 3, 32, 32)   ,					rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)
           if str(UpperCAmelCase__).startswith('''mps'''):
                  A__ 					=       torch.manual_seed(UpperCAmelCase__)
           else:
                  A__ 					=       torch.Generator(device=UpperCAmelCase__).manual_seed(UpperCAmelCase__)

           if pil_image:
                  A__ 					=       input_image * 0.5 + 0.5
                  A__ 					=       input_image.clamp(0   ,					1)
                  A__ 					=       input_image.cpu().permute(0   ,					2   ,					3   ,					1).float().numpy()
                  A__ 					=       DiffusionPipeline.numpy_to_pil(UpperCAmelCase__)[0]

           return {
               "image": input_image,
               "generator": generator,
               "decoder_num_inference_steps": 2,
               "super_res_num_inference_steps": 2,
               "output_type": "np",
           }


    def 			SCREAMING_SNAKE_CASE				(     self :      str)		->int:





           '''simple docstring'''

           A__ 					=       '''cpu'''

           A__ 					=       self.get_dummy_components()

           A__ 					=       self.pipeline_class(**UpperCAmelCase__)
           A__ 					=       pipe.to(UpperCAmelCase__)

           pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__ 					=       self.get_dummy_inputs(UpperCAmelCase__   ,					pil_image=UpperCAmelCase__)

           A__ 					=       pipe(**UpperCAmelCase__)
           A__ 					=       output.images

           A__ 					=       self.get_dummy_inputs(UpperCAmelCase__   ,					pil_image=UpperCAmelCase__)

           A__ 					=       pipe(
               **UpperCAmelCase__   ,					return_dict=UpperCAmelCase__   ,					)[0]

           A__ 					=       image[0, -3:, -3:, -1]
           A__ 					=       image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           A__ 					=       np.array(
               [
                   0.9997,
                   0.0002,
                   0.9997,
                   0.9997,
                   0.9969,
                   0.0023,
                   0.9997,
                   0.9969,
                   0.9970,
               ])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->str:





           '''simple docstring'''

           A__ 					=       '''cpu'''

           A__ 					=       self.get_dummy_components()

           A__ 					=       self.pipeline_class(**UpperCAmelCase__)
           A__ 					=       pipe.to(UpperCAmelCase__)

           pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__ 					=       self.get_dummy_inputs(UpperCAmelCase__   ,					pil_image=UpperCAmelCase__)

           A__ 					=       pipe(**UpperCAmelCase__)
           A__ 					=       output.images

           A__ 					=       self.get_dummy_inputs(UpperCAmelCase__   ,					pil_image=UpperCAmelCase__)

           A__ 					=       pipe(
               **UpperCAmelCase__   ,					return_dict=UpperCAmelCase__   ,					)[0]

           A__ 					=       image[0, -3:, -3:, -1]
           A__ 					=       image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           A__ 					=       np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->int:





           '''simple docstring'''

           A__ 					=       '''cpu'''

           A__ 					=       self.get_dummy_components()

           A__ 					=       self.pipeline_class(**UpperCAmelCase__)
           A__ 					=       pipe.to(UpperCAmelCase__)

           pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__ 					=       self.get_dummy_inputs(UpperCAmelCase__   ,					pil_image=UpperCAmelCase__)
           A__ 					=       [
               pipeline_inputs['''image'''],
               pipeline_inputs['''image'''],
           ]

           A__ 					=       pipe(**UpperCAmelCase__)
           A__ 					=       output.images

           A__ 					=       self.get_dummy_inputs(UpperCAmelCase__   ,					pil_image=UpperCAmelCase__)
           A__ 					=       [
               tuple_pipeline_inputs['''image'''],
               tuple_pipeline_inputs['''image'''],
           ]

           A__ 					=       pipe(
               **UpperCAmelCase__   ,					return_dict=UpperCAmelCase__   ,					)[0]

           A__ 					=       image[0, -3:, -3:, -1]
           A__ 					=       image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (2, 64, 64, 3)

           A__ 					=       np.array(
               [
                   0.9997,
                   0.9989,
                   0.0008,
                   0.0021,
                   0.9960,
                   0.0018,
                   0.0014,
                   0.0002,
                   0.9933,
               ])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2


    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any])		->Any:





           '''simple docstring'''

           A__ 					=       torch.device('''cpu''')


           class 							UpperCamelCase_       :

               '''simple docstring'''






               UpperCAmelCase__				    = 1
           A__ 					=       self.get_dummy_components()

           A__ 					=       self.pipeline_class(**UpperCAmelCase__)
           A__ 					=       pipe.to(UpperCAmelCase__)

           pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__ 					=       torch.Generator(device=UpperCAmelCase__).manual_seed(0)
           A__ 					=       pipe.decoder.dtype
           A__ 					=       1

           A__ 					=       (
               batch_size,
               pipe.decoder.config.in_channels,
               pipe.decoder.config.sample_size,
               pipe.decoder.config.sample_size,
           )
           A__ 					=       pipe.prepare_latents(
               UpperCAmelCase__   ,					dtype=UpperCAmelCase__   ,					device=UpperCAmelCase__   ,					generator=UpperCAmelCase__   ,					latents=UpperCAmelCase__   ,					scheduler=DummyScheduler())

           A__ 					=       (
               batch_size,
               pipe.super_res_first.config.in_channels // 2,
               pipe.super_res_first.config.sample_size,
               pipe.super_res_first.config.sample_size,
           )
           A__ 					=       pipe.prepare_latents(
               UpperCAmelCase__   ,					dtype=UpperCAmelCase__   ,					device=UpperCAmelCase__   ,					generator=UpperCAmelCase__   ,					latents=UpperCAmelCase__   ,					scheduler=DummyScheduler())

           A__ 					=       self.get_dummy_inputs(UpperCAmelCase__   ,					pil_image=UpperCAmelCase__)

           A__ 					=       pipe(
               **UpperCAmelCase__   ,					decoder_latents=UpperCAmelCase__   ,					super_res_latents=UpperCAmelCase__).images

           A__ 					=       self.get_dummy_inputs(UpperCAmelCase__   ,					pil_image=UpperCAmelCase__)
           # Don't pass image, instead pass embedding
           A__ 					=       pipeline_inputs.pop('''image''')
           A__ 					=       pipe.image_encoder(UpperCAmelCase__).image_embeds

           A__ 					=       pipe(
               **UpperCAmelCase__   ,					decoder_latents=UpperCAmelCase__   ,					super_res_latents=UpperCAmelCase__   ,					image_embeddings=UpperCAmelCase__   ,					).images

           # make sure passing text embeddings manually is identical
           assert np.abs(img_out_a - img_out_a).max() < 1e-4


    @skip_mps
    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->int:





           '''simple docstring'''

           A__ 					=       torch_device == '''cpu'''

           # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
           A__ 					=       1e-2

           self._test_attention_slicing_forward_pass(
               test_max_difference=UpperCAmelCase__   ,					expected_max_diff=UpperCAmelCase__)


    @skip_mps
    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->Tuple:





           '''simple docstring'''

           A__ 					=       torch_device == '''cpu'''
           A__ 					=       True
           A__ 					=       [
               '''decoder_num_inference_steps''',
               '''super_res_num_inference_steps''',
           ]

           self._test_inference_batch_single_identical(
               test_max_difference=UpperCAmelCase__   ,					relax_max_difference=UpperCAmelCase__   ,					additional_params_copy_to_batched_inputs=UpperCAmelCase__   ,					)


    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->Union[str, Any]:





           '''simple docstring'''

           A__ 					=       [
               '''decoder_num_inference_steps''',
               '''super_res_num_inference_steps''',
           ]

           if torch_device == "mps":
                  # TODO: MPS errors with larger batch sizes
                  A__ 					=       [2, 3]
                  self._test_inference_batch_consistent(
                      batch_sizes=UpperCAmelCase__   ,					additional_params_copy_to_batched_inputs=UpperCAmelCase__   ,					)
           else:
                  self._test_inference_batch_consistent(
                      additional_params_copy_to_batched_inputs=UpperCAmelCase__)


    @skip_mps
    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->int:





           '''simple docstring'''

           return super().test_dict_tuple_outputs_equivalent()


    @skip_mps
    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->int:





           '''simple docstring'''

           return super().test_save_load_local()





    @skip_mps
    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Dict:





           '''simple docstring'''

           return super().test_save_load_optional_components()


@slow
@require_torch_gpu
class 							UpperCamelCase_       (					unittest.TestCase	):

    '''simple docstring'''






    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->Dict:





           '''simple docstring'''

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()





    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->Any:





           '''simple docstring'''

           A__ 					=       load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png''')
           A__ 					=       load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy''')

           A__ 					=       UnCLIPImageVariationPipeline.from_pretrained(
               '''kakaobrain/karlo-v1-alpha-image-variations'''   ,					torch_dtype=torch.floataa)
           A__ 					=       pipeline.to(UpperCAmelCase__)
           pipeline.set_progress_bar_config(disable=UpperCAmelCase__)

           A__ 					=       torch.Generator(device='''cpu''').manual_seed(0)
           A__ 					=       pipeline(
               UpperCAmelCase__   ,					generator=UpperCAmelCase__   ,					output_type='''np'''   ,					)

           A__ 					=       output.images[0]

           assert image.shape == (256, 256, 3)

           assert_mean_pixel_difference(UpperCAmelCase__   ,					UpperCAmelCase__   ,					15)



import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator


def 				lowerCAmelCase__       (			a__ )      ->int:




  '''simple docstring'''

  _UpperCamelCase           =    fname.split(os.path.sep )[-1]
  return re.search(r"^(.*)_\d+\.jpg$"    ,     a__ ).groups()[0]




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   int							, lowercase_				:   int							, lowercase_				:   Dict=None							, lowercase_				:   Optional[Any]=None)						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    file_names
   _UpperCamelCase           =    image_transform
   _UpperCamelCase           =    label_to_id







 def __len__(		self				:   int)						->	Optional[int]:


   """simple docstring"""







   return len(self.file_names)







 def __getitem__(		self				:   int							, lowercase_				:   int)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    self.file_names[idx]
   _UpperCamelCase           =    PIL.Image.open(lowercase_)
   _UpperCamelCase           =    raw_image.convert("RGB")
   if self.image_transform is not None:
     _UpperCamelCase           =    self.image_transform(lowercase_)
   _UpperCamelCase           =    extract_label(lowercase_)
   if self.label_to_id is not None:
     _UpperCamelCase           =    self.label_to_id[label]
   return {"image": image, "label": label}


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->List[Any]:




  '''simple docstring'''

  if args.with_tracking:
    _UpperCamelCase           =    Accelerator(
        cpu=args.cpu    ,     mixed_precision=args.mixed_precision    ,     log_with="all"    ,     project_dir=args.project_dir )
  else:
    _UpperCamelCase           =    Accelerator(cpu=args.cpu    ,     mixed_precision=args.mixed_precision )

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _UpperCamelCase           =    config["lr"]
  _UpperCamelCase           =    int(config["num_epochs"] )
  _UpperCamelCase           =    int(config["seed"] )
  _UpperCamelCase           =    int(config["batch_size"] )
  _UpperCamelCase           =    config["image_size"]
  if not isinstance(a__    ,     (list, tuple) ):
    _UpperCamelCase           =    (image_size, image_size)

  # Parse out whether we are saving every epoch or after a certain number of batches
  if hasattr(args.checkpointing_steps    ,     "isdigit" ):
    if args.checkpointing_steps == "epoch":
      _UpperCamelCase           =    args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      _UpperCamelCase           =    int(args.checkpointing_steps )
    else:
      raise ValueError(
          f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.' )
  else:
    _UpperCamelCase           =    None

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    _UpperCamelCase           =    os.path.split(a__ )[-1].split("." )[0]
    accelerator.init_trackers(a__    ,     a__ )

  # Grab all the image filenames
  _UpperCamelCase           =    [os.path.join(args.data_dir    ,     a__ ) for fname in os.listdir(args.data_dir ) if fname.endswith(".jpg" )]

  # Build the label correspondences
  _UpperCamelCase           =    [extract_label(a__ ) for fname in file_names]
  _UpperCamelCase           =    list(set(a__ ) )
  id_to_label.sort()
  _UpperCamelCase           =    {lbl: i for i, lbl in enumerate(a__ )}

  # Set the seed before splitting the data.
  np.random.seed(a__ )
  torch.manual_seed(a__ )
  torch.cuda.manual_seed_all(a__ )

  # Split our filenames between train and validation
  _UpperCamelCase           =    np.random.permutation(len(a__ ) )
  _UpperCamelCase           =    int(0.8 * len(a__ ) )
  _UpperCamelCase           =    random_perm[:cut]
  _UpperCamelCase           =    random_perm[cut:]

  # For training we use a simple RandomResizedCrop
  _UpperCamelCase           =    Compose([RandomResizedCrop(a__    ,     scale=(0.5, 1.0) ), ToTensor()] )
  _UpperCamelCase           =    PetsDataset(
      [file_names[i] for i in train_split]    ,     image_transform=a__    ,     label_to_id=a__ )

  # For evaluation, we use a deterministic Resize
  _UpperCamelCase           =    Compose([Resize(a__ ), ToTensor()] )
  _UpperCamelCase           =    PetsDataset([file_names[i] for i in eval_split]    ,     image_transform=a__    ,     label_to_id=a__ )

  # Instantiate dataloaders.
  _UpperCamelCase           =    DataLoader(a__    ,     shuffle=a__    ,     batch_size=a__    ,     num_workers=4 )
  _UpperCamelCase           =    DataLoader(a__    ,     shuffle=a__    ,     batch_size=a__    ,     num_workers=4 )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _UpperCamelCase           =    create_model("resnet50d"    ,     pretrained=a__    ,     num_classes=len(a__ ) )

  # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
  # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
  # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
  _UpperCamelCase           =    model.to(accelerator.device )

  # Freezing the base model
  for param in model.parameters():
    _UpperCamelCase           =    False
  for param in model.get_classifier().parameters():
    _UpperCamelCase           =    True

  # We normalize the batches of images to be a bit faster.
  _UpperCamelCase           =    torch.tensor(model.default_cfg["mean"] )[None, :, None, None].to(accelerator.device )
  _UpperCamelCase           =    torch.tensor(model.default_cfg["std"] )[None, :, None, None].to(accelerator.device )

  # Instantiate optimizer
  _UpperCamelCase           =    torch.optim.Adam(params=model.parameters()    ,     lr=lr / 25 )

  # Instantiate learning rate scheduler
  _UpperCamelCase           =    OneCycleLR(optimizer=a__    ,     max_lr=a__    ,     epochs=a__    ,     steps_per_epoch=len(a__ ) )

  # Prepare everything
  # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  # prepare method.
  _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    accelerator.prepare(
      a__    ,     a__    ,     a__    ,     a__    ,     a__ )
  # We need to keep track of how many total steps we have iterated over
  _UpperCamelCase           =    0
  # We also need to keep track of the starting epoch so files are named properly
  _UpperCamelCase           =    0

  # Potentially load in the weights and states from a previous save
  if args.resume_from_checkpoint:
    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}' )
      accelerator.load_state(args.resume_from_checkpoint )
      _UpperCamelCase           =    os.path.basename(args.resume_from_checkpoint )
    else:
      # Get the most recent checkpoint
      _UpperCamelCase           =    [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
      dirs.sort(key=os.path.getctime )
      _UpperCamelCase           =    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    _UpperCamelCase           =    os.path.splitext(a__ )[0]

    if "epoch" in training_difference:
      _UpperCamelCase           =    int(training_difference.replace("epoch_"    ,     "" ) ) + 1
      _UpperCamelCase           =    None
    else:
      _UpperCamelCase           =    int(training_difference.replace("step_"    ,     "" ) )
      _UpperCamelCase           =    resume_step // len(a__ )
      resume_step -= starting_epoch * len(a__ )

    # Now we train the model
  for epoch in range(a__    ,     a__ ):
    model.train()
    if args.with_tracking:
      _UpperCamelCase           =    0
    if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
      # We need to skip steps until we reach the resumed step
      _UpperCamelCase           =    accelerator.skip_first_batches(a__    ,     a__ )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      _UpperCamelCase           =    train_dataloader
    for batch in active_dataloader:
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      _UpperCamelCase           =    {k: v.to(accelerator.device ) for k, v in batch.items()}
      _UpperCamelCase           =    (batch["image"] - mean) / std
      _UpperCamelCase           =    model(a__ )
      _UpperCamelCase           =    torch.nn.functional.cross_entropy(a__    ,     batch["label"] )
      # We keep track of the loss at each epoch
      if args.with_tracking:
        total_loss += loss.detach().float()
      accelerator.backward(a__ )
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()
      overall_step += 1
      if isinstance(a__    ,     a__ ):
        _UpperCamelCase           =    f'step_{overall_step}'
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            _UpperCamelCase           =    os.path.join(args.output_dir    ,     a__ )
          accelerator.save_state(a__ )
    model.eval()
    _UpperCamelCase           =    0
    _UpperCamelCase           =    0
    for step, batch in enumerate(a__ ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      _UpperCamelCase           =    {k: v.to(accelerator.device ) for k, v in batch.items()}
      _UpperCamelCase           =    (batch["image"] - mean) / std
      with torch.no_grad():
        _UpperCamelCase           =    model(a__ )
      _UpperCamelCase           =    outputs.argmax(dim=-1 )
      _UpperCamelCase      ,						_UpperCamelCase           =    accelerator.gather_for_metrics((predictions, batch["label"]) )
      _UpperCamelCase           =    predictions == references
      num_elems += accurate_preds.shape[0]
      accurate += accurate_preds.long().sum()

    _UpperCamelCase           =    accurate.item() / num_elems
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'epoch {epoch}: {100 * eval_metric:.2f}' )
    if args.with_tracking:
      accelerator.log(
          {
              "accuracy": 100 * eval_metric,
              "train_loss": total_loss.item() / len(a__ ),
              "epoch": epoch,
          }    ,     step=a__    ,     )
    if checkpointing_steps == "epoch":
      _UpperCamelCase           =    f'epoch_{epoch}'
      if args.output_dir is not None:
        _UpperCamelCase           =    os.path.join(args.output_dir    ,     a__ )
      accelerator.save_state(a__ )

  if args.with_tracking:
    accelerator.end_training()


def 				lowerCAmelCase__       (			)      ->Any:




  '''simple docstring'''

  _UpperCamelCase           =    argparse.ArgumentParser(description="Simple example of training script." )
  parser.add_argument("--data_dir"    ,     required=a__    ,     help="The data folder on disk." )
  parser.add_argument("--fp16"    ,     action="store_true"    ,     help="If passed, will use FP16 training." )
  parser.add_argument(
      "--mixed_precision"    ,     type=a__    ,     default=a__    ,     choices=["no", "fp16", "bf16", "fp8"]    ,     help="Whether to use mixed precision. Choose"
      "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
      "and an Nvidia Ampere GPU."    ,     )
  parser.add_argument("--cpu"    ,     action="store_true"    ,     help="If passed, will train on the CPU." )
  parser.add_argument(
      "--checkpointing_steps"    ,     type=a__    ,     default=a__    ,     help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."    ,     )
  parser.add_argument(
      "--output_dir"    ,     type=a__    ,     default="."    ,     help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."    ,     )
  parser.add_argument(
      "--resume_from_checkpoint"    ,     type=a__    ,     default=a__    ,     help="If the training should continue from a checkpoint folder."    ,     )
  parser.add_argument(
      "--with_tracking"    ,     action="store_true"    ,     help="Whether to load in all available experiment trackers from the environment and use them for logging."    ,     )
  parser.add_argument(
      "--project_dir"    ,     type=a__    ,     default="logs"    ,     help="Location on where to store experiment tracking logs` and relevent project information"    ,     )
  _UpperCamelCase           =    parser.parse_args()
  _UpperCamelCase           =    {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
  training_function(a__    ,     a__ )


if __name__ == "__main__":
   main()




